Synthesis, characterization, DFT and in silico anti-ulcer activities of novel sulphonamide derivatives

Abstract

Peptic ulcer disease (PUD) is a major gastrointestinal disorder associated with Helicobacter pylori infection and long-term use of non-steroidal anti-inflammatory drugs (NSAIDs). Current treatment strategies face limitations due to drug resistance, recurrence, and adverse effects, highlighting the need for novel therapeutic scaffolds. In this study, four newly designed sulfonamide derivatives (CPD-1 to CPD-4) were synthesized, characterized using state-of-the art instruments, and evaluated for anti-ulcer activities through a comprehensive computational chemistry approach. Density functional theory (DFT) optimizations with B3LYP, ωB97XD, and M06-2X functionals confirmed molecular stability and reactivity. Frontier molecular orbital (FMO) analysis revealed small HOMO–LUMO gaps, with CPD-1 exhibiting the lowest energy (3.71 eV), suggesting high chemical reactivity. Natural bond orbital (NBO) analysis indicated strong intramolecular charge transfer and stabilization, while molecular electrostatic potential (MESP) maps identified regions favorable for electrophilic and nucleophilic interactions. Non-covalent interaction (NCI) plots further supported stabilization through hydrogen bonding and van der Waals interactions. Docking studies against H. pylori CagI protein (PDB ID: 8AK1) showed CPD-1 had the strongest binding affinity (− 7.2 kcal/mol), stabilized by five hydrogen bonds, surpassing the reference drug DB00338. ADMET predictions revealed that CPD-2 had the most favorable pharmacokinetic and safety profile, with acceptable clearance, moderate plasma protein binding, and compliance with Lipinski’s rules. Collectively, these results suggest that CPD-1, CPD-2, and CPD-3 are promising candidates for further development as anti-ulcer agents. This integrative approach demonstrates the power of combining DFT, docking, and ADMET modeling for the rational design of novel therapeutics targeting H. pylori.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-45373-9.

Introduction

Peptic ulcer disease (PUD) remains a significant gastrointestinal problem despite progress in therapy. It is caused by an imbalance between protective mucosal factors and aggressive influences such as gastric acid, pepsin, Helicobacter pylori infection, and non-steroidal anti-inflammatory drugs (NSAIDs). Globally, PUD continues to affect millions of people and contributes to substantial morbidity^1,2. Several classes of drugs are available for ulcers curative. Proton pump inhibitors (PPIs) such as omeprazole and esomeprazole remain the mainstay, while histamine-2 receptor antagonists and antacids provide additional acid suppression. Mucosal protectants like sucralfate and misoprostol are used particularly in NSAID-related ulcers. For H. pylori, combination therapies with antibiotics and bismuth salts are standard. More recently, potassium-competitive acid blockers (P-CABs) such as vonoprazan have shown stronger acid inhibition and faster onset compared to PPIs³.

However, several challenges justify the search for new anti-ulcer drugs. First, rising H. pylori antibiotic resistance has reduced the success of conventional eradication regimens, creating demand for novel antimicrobials or antivirulence agents^2,4. Second, long-term use of PPIs has been associated with potential adverse outcomes, including infections, nutrient deficiencies, and kidney complications, raising safety concerns for chronic therapy⁵. Third, patients requiring long-term NSAIDs, as well as the elderly with co-morbidities, remain at higher risk for recurrent ulcers even on standard therapy⁶.

Recent studies have turned to computational approaches to accelerate discovery. In-silico methods such as virtual screening, molecular docking, and molecular dynamics help identify novel targets and predict compound interactions with bacterial enzymes like urease and DAH7PS, essential for H. pylori survival⁷. New reports have highlighted natural product-inspired inhibitors and synthetic scaffolds with promising activity against resistant strains^8,9. These computational pipelines enable rapid prioritization of drug candidates while reducing costs and time, making them valuable for designing safer, more effective anti-ulcer therapies. Given the serious health complications associated with Helicobacter pylori infection and the growing challenge of limited therapeutic options, this study investigates potential treatment strategies using computational chemistry. Density Functional Theory (DFT) is applied to assess the inhibitory potential of four newly designed sulphonamide compounds, CPD-1, CPD-2, CPD-3, and CPD-4. The analysis focuses on their ability to suppress bacterial activity and reduce disease-related symptoms. By employing theoretical modelling, this research aims to advance the search for effective therapies against H. pylori, offering a basis for future experimental validation and clinical application.

Materials and methods for experimental design

General instrumentation and reagents

All reagents and chemicals used were of analytical grade and were purchased from Thomas Baker Chemical Pvt. Ltd., Mumbai, Avra Chemicals Pvt. Ltd., Hyderabad, and Finar Limited, Ahmedabad, India, and were used without further purification. Melting points were determined using a glass capillary tube with Stuart’s melting point apparatus and were uncorrected. Silica plates were used for thin-layer chromatography, and the spots were visualized under UV light, stained with ninhydrin/HBr, and dried in an electric oven. Fourier Transform Infrared (FT-IR) spectra were recorded on a Thermo Scientific Nicolet iS20 FTIR spectrometer (model 2024), with absorptions reported in centimetres (cm^− 1). The ¹H-NMR and¹³C-NMR spectra were recorded in deuterated dimethyl sulphoxide, DMSO-d6, using a Varian New 500 MHz spectrometer, and the chemical shifts were recorded in ppm. Mass spectra were determined using a micro electrospray time-of-flight (ESI-TOF) spectrometer, and Sodium Formate was used as the calibrant at the Indian Institute of Technology, Kanpur, India.

All laboratory synthesis was carried out in Prof Sandeep Verma’s research laboratory, Chemistry Department of Indian Institute of Technology, Kanpur, Uttar Pradesh, India.

Procedure for synthesis of para toluene sulphonamide carboxamide derivatives

The experimental procedure involves the reaction of L-Threonine (2.680 g, 22.5mmol) with para toluene sulphonyl chloride (15 mmol) in the presence of a base, sodium carbonate (2.385 g, 22.5 mmol), to give the sulphonamide. The mixture was allowed to stir overnight at room temperature; TLC was used to monitor the reaction. On completion, the mixture was acidified with 20% concentrated HCl to pH 2–2.2 and then extracted with ethyl acetate (100 mL) and water (50 mL). The organic layer was washed with water twice and then with brine solution; it was collected over sodium sulphate and evaporated using a rotary evaporator to obtain a solid, which was further dried in a desiccator for 12 h. The para toluene sulphonamide derivatives bearing L-Threonine were obtained in good to excellent yields (63.51% − 72.83%) (first intermediate).

Boc-threonine (0.560 g, 3.2 mmol) was dissolved in 20mL dichloromethane (DCM), and triethylamine (TEA) was added (6.4 mmol) and cooled to −2 °C. 1-(3- dimethylaminopropyl)−3-ethylcarbodiimide hydrochloride (EDC.HCl) (1.226 g, 6.4mmol), hydroxybenzotriazole, HOBt (0.432 g, 3.2 mmol) were added and stirred for 10 min. Butyramide (4.8 mmol) was added to the solution and stirred for 30 min. The mixture was stirred at room temperature for 22 h and monitored with TLC. On completion, the reaction mixture was diluted with distilled water (50 ml), and an aqueous work up performed. The organic layer was washed with 1 N HCl (50mL), 10%NaHCO₃ (50mL) and brine solution (50 ml). The organic layer was collected over sodium sulphate and then evaporated in a rotary evaporator. The resulting concentrate was deprotected by adding trifluoroacetic acid, TFA/DCM (1:1), and stirred at room temperature for 1 h. On completion, the reaction mixture was evaporated in a rotary evaporator, 50mL of diethyl ether was added and evaporated, and thereafter, methanol (50mL) was added and evaporated; these solvents were added to remove traces of TFA. Purification by column chromatography was done to give the desired product in pure form (second intermediate).

Para toluene sulphonamide (3.2 mmol) from the first stage was dissolved in 20mL DMF, TEA (9.6 mmol) was added, and the solution was cooled to −20 °C. EDC.HCl (6.4 mmol) and HOBt (6.4 mmol) were added and allowed to stir for 10 min. The carboxamide derivatives (4.8 mmol) from the second stage were added to the reaction mixture and stirred for 30 min. This was then stirred at room temperature for 20–22 h while being monitored with TLC. On completion, the reaction mixture was diluted with water (50 mL) and ethyl acetate (50mL), and aqueous work up performed. The organic layer was washed with 1 N HCl, 10% NaHCO_3, and brine solution and then collected over sodium sulphate. The organic layer was evaporated in a rotary evaporator to give the desired product, which was then purified by column chromatography to provide the pure compound (CPD1-4). The above protocol was used to synthesize all the derivatives Fig. 1 and Scheme 1:

Fig. 1.

(a) N-(4-chlorophenyl)−3-hydroxy-2-(3-hydroxy-2-((4 methylphenyl) sulfonamido) butanamido) butanamide. FT-IR: 3675, 3363, 2985, 1685, 1540, 1339. ¹H NMR (500 MHz, DMSO-d₆) δ 9.68 (s, 1H), 7.84 (d, J = 8.4 Hz, 1H), 7.65 (d, J = 8.3 Hz, 2 H), 7.60 (d, J = 8.9 Hz, 2 H), 7.29 (dd, J = 25.6, 8.4 Hz, 4 H), 5.71 (s, 1H), 5.18 (d, J = 4.8 Hz, 1H), 4.95 (d, J = 5.1 Hz, 1H), 4.13 (dd, J = 8.4, 3.1 Hz, 1H), 4.04 (dd, J = 10.4, 4.2 Hz, 1H), 3.90–3.78 (m, 2 H), 2.29 (s, 3 H), 0.92 (dd, J = 23.9, 6.3 Hz, 6 H). ¹³C NMR (126 MHz, DMSO-d₆) δ 169.74, 169.42, 143.11, 138.23, 138.15, 129.87, 129.15, 127.53, 127.28, 121.24, 68.10, 66.77, 61.57, 59.40, 55.44, 21.47, 20.46, 19.19. HRMS (m/z): C₂₁H₂₆ClN₃NaO₆S calculated: 506.1129; found 506.1124. (b) 3-hydroxy-N-(3-hydroxy-1-((4-methoxyphenyl) amino)−1-oxobutan-2-yl)−2-((4-methylphenyl) sulfonamido) butanamide. FT-IR; 3617, 3374, 2984, 1716, 1541, 1339. ¹H NMR (500 MHz, DMSO-d₆) δ 9.42 (s, 1H), 7.91 (s, 1H), 7.66 (d, J = 8.3 Hz, 2 H), 7.49 (d, J = 8.9 Hz, 2 H), 7.27 (d, J = 8.1 Hz, 2 H), 6.83 (d, J = 9.0 Hz, 2 H), 5.26 (s, 1H), 4.94 (d, J = 4.9 Hz, 1H), 4.15–4.01 (m, 2 H), 3.92–3.77 (m, 2 H), 3.67 (s, 3 H), 2.85 (s, 2 H), 2.69 (s, 2 H), 2.31 (d, J = 17.0 Hz, 3 H), 0.91 (dd, J = 21.4, 6.3 Hz, 6 H). ¹³C NMR (126 MHz, DMSO-d₆) δ 169.69, 168.73, 162.86, 155.84, 143.14, 138.14, 132.41, 129.88, 127.30, 121.17, 114.33, 68.08, 66.77, 61.67, 59.38, 55.71, 21.48, 20.48, 19.16. HRMS (m/z): C₂₂H₂₉N₃NaO₇S calculated: 502.1624; found 502.1623. (c) 3-hydroxy-N-(3-hydroxy-1-morpholino-1-oxobutan-2-yl)−2-((4 methylphenyl) sulfonamido) butanamide. FT-IR: 3586, 3255, 2978, 1733, 1558, 1323. ¹H NMR (500 MHz, DMSO-d₆) δ 9.07 (d, J = 10.2 Hz, 1H), 8.00–7.86 (m, 3 H), 7.80 (d, J = 7.8 Hz, 2 H), 7.77–7.69 (m, 2 H), 7.64–7.52 (m, 6 H), 5.37 (dd, J = 10.1, 6.0 Hz, 1H), 4.98 (d, J = 3.9 Hz, 1H), 4.77–4.63 (m, 1H), 4.48–4.31 (m, 2 H), 3.99 (q, J = 7.1 Hz, 2 H), 3.84–3.69 (m, 2 H), 1.95 (s, 2 H), 1.13 (t, J = 7.1 Hz, 2 H), 1.07–1.03 (m, 3 H), 0.94 (dd, J = 12.3, 6.4 Hz, 4 H), 0.77 (d, J = 6.1 Hz, 2 H). ¹³C NMR (126 MHz, DMSO-d₆) δ 171.28, 169.45, 141.58, 133.49, 129.98, 128.53, 126.77, 126.11, 94.99, 88.48, 81.85, 77.75, 76.57, 75.14, 61.76, 60.28, 21.28, 19.62, 17.40, 15.00. HRMS (m/z): C₁₉H₂₉N₃NaO₇S calculated: 466.1624; found 466.162. (d) 3-hydroxy-N-(3-hydroxy-1-oxo-1-(pyridin-2-ylamino)butan-2-yl)−2-((4-methylphenyl) sulfonamido) butanamide FT-IR: 3566, 3254, 2978, 1868, 1575, 1328. ¹H NMR (500 MHz, DMSO-d₆) δ 8.98 (d, J = 10.3 Hz, 1H), 8.06–7.97 (m, 1H), 7.78 (d, J = 8.4 Hz, 1H), 7.66 (d, J = 8.3 Hz, 2 H), 7.59–7.52 (m, 1H), 7.39 (t, J = 7.8 Hz, 3 H), 5.33 (dd, J = 10.2, 6.0 Hz, 1H), 4.76–4.65 (m, 1H), 3.99 (q, J = 7.1 Hz, 2 H), 2.35 (s, 3 H), 1.95 (s, 2 H), 1.41 (s, 3 H), 1.13 (t, J = 7.1 Hz, 3 H), 1.04 (d, J = 6.4 Hz, 3 H). ¹³C NMR (126 MHz, DMSO-d₆) δ 170.84, 169.49, 143.80, 138.78, 130.35, 130.09, 129.81, 128.87, 127.13, 126.84, 125.17, 120.32, 110.66, 90.35, 86.86, 75.16, 61.76, 60.27, 59.51, 52.41, 27.44, 21.51, 14.61. HRMS (m/z): C₂₀H₂₆N₄NaO₆S calculated: 473.1471; found 473.1473.

Scheme 1.

Synthesis of sulphonamide dipeptide carboxamide derivatives. R = CH₃. Reagents and conditions: i). Na₂CO₃, HCl, −2 °C, r.t, overnight. ii). EDC.HCl, HOBt, TEA, DCM, 0 °C, r.t, 20–22 hr iii). TFA/DCM (1:1), 2hr. iv). EDC.HCl, HOBt, TEA, DMF, 0 °C, r.t, 20–22 hr. NHR’R’’= Primary and secondary amines.

Computational design

Geometry optimization of sulfonamide derivatives was carried out using density functional theory (DFT) with three different functionals: B3LYP, ωB97XD, and M06-2X, in combination with appropriate basis sets (6-31G+ (d, p), def2-TZVP, and 6-311G++ (d, p), respectively). The B3LYP functional (Becke’s three-parameter hybrid functional with the Lee–Yang–Parr correlation) was selected because of its reliability and widespread application in predicting optimized geometries, vibrational frequencies, and electronic structures of organic molecules, including sulfonamides^10–14. ωB97XD, a long-range corrected hybrid functional with empirical dispersion, was employed to account for non-covalent interactions and long-range electron correlation effects, which are important in sulfonamide systems due to intramolecular hydrogen bonding and weak van der Waals forces¹⁵. M06-2X, a meta-hybrid functional with high nonlocal exchange, was chosen for its strong performance in main-group thermochemistry and non-covalent interaction modelling, providing better accuracy in calculating geometries and interaction energies¹⁶. Regarding basis sets, 6-31G+ (d, p) was used for preliminary geometry optimization because it balances computational cost with reasonable accuracy for ground-state molecular structures. def2-TZVP was applied with ωB97XD due to its high accuracy in describing electron correlation and flexibility across a wide range of systems, particularly for non-covalent interactions¹⁷. 6-311G++ (d, p), a triple-zeta basis set with diffuse and polarization functions, was used with M06-2X to capture electron density more accurately in regions far from nuclei, improving the description of intramolecular interactions and hydrogen bonding present in sulfonamide derivatives¹⁸. The quantum chemical calculations for the investigated sulfonamide compounds were carried out using the Gaussian 09 W software package¹⁹, while the initial molecular structures were built and visualized with GaussView 6.0.16²⁰. The frontier molecular orbital (FMO) analysis was employed to evaluate the electronic properties, chemical reactivity, and stability of the sulfonamide derivatives. In addition, quantumchemical descriptors were calculated to provide deeper insight into their potential reactivity patterns and biological activity^21,22. The HOMO–LUMO iso-surface visualizations were generated using the Chemcraft 1.0 software²³. Charge transfer interactions were examined using Natural Bond Orbital (NBO)analysis, performed with the NBO3.1 program integrated into the Gaussian software package²⁴. The molecular electrostatic potential (MESP) maps, which illustrate the charge distribution and electrostatic potential of the sulfonamide molecules, were generated using the GaussView 6.0.16 visualization software²⁵. Weak interactions in the studied compounds were examined using non-covalent interaction (NCI) analysis, where the reduced density gradient (RDG) plots were generated with the Multiwfn 3.7 software package²⁶.

Molecular docking protocol

Molecular docking is a computational technique used to predict the preferred orientation of a molecule (ligand) when bound to a target protein, as well as to estimate the strength of the interaction²⁷. The process involves positioning the ligand into the protein’s active or binding site and evaluating the resulting complex using scoring functions that estimate binding affinity based on factors such as hydrogen bonding, hydrophobic interactions, van der Waals forces, and electrostatics. Molecular docking serves as a cornerstone in structure-based drug design and discovery, allowing researchers to predict how ligands interact with protein targets by estimating both binding conformation and affinity²⁸. For the molecular docking analysis (see Figs. 5 and 6), four sulfonamide ligands (CPD-1, CPD-2, CPD-3, and CPD-4) were selected to evaluate their therapeutic potential against ulcer. The receptor protein used in the study was retrieved from the Protein Data Bank with PDB ID: 8AK1²⁹. The protein 8AK1 used for the binding study was downloaded from the protein data bank, obtained from the online protein database (www.RSCPDB.org) in PDB format. The protein was prepared using Biovia Discovery Studio2021software, where water molecules and native ligand were deleted, and polar hydrogen was introduced³⁰. Auto-Dock Vina4.2 softwarewas used to simulate the prepared protein’s interaction with the ligands, adding a pdbqt charge to both the protein and the ligands, which gave the binding affinity³¹. The grid coordinate (x, y, and z) and radii (r) for the protein are x = −0.736000, y = −6.499714, and z = 12.396143, r= 21.000000, which define the active site search space on the receptor protein where the docking algorithm will place and evaluate the ligands³². The docking outcomes were analyzed and visualized with BioviaDiscovery Studio 2021, which generated two-dimensional interaction diagrams, and PyMOL, which provided three-dimensional structural representations of the ligand–protein complexes^33,34. In addition, the protein structure with PDB ID: 8AK1 was selected as the receptor for molecular docking because of its central role in Helicobacter pylori pathogenesis, which is a primary cause of peptic ulcer disease³⁵. This protein corresponds to CagI, a crucial component of the H. pylori type IV secretion system (cagT4SS). The cagT4SS mediates the delivery of the virulence factor CagA into gastric epithelial cells, leading to inflammation, mucosal damage, and increased risk of ulcer development³⁶.

ADMET protocol

ADMET profiling was conducted to assess the toxicity, physicochemical characteristics, and drug-likeness of the investigated compounds. For toxicity evaluation, ProTox3.0³⁷ was employed, while SwissADME was used to determine physicochemical parameters and drug-likeness³⁸. In addition, ADMETlab 3.0 was incorporated to provide a comprehensive prediction of pharmacokinetic and toxicity properties³⁹. The compounds were first converted into SMILES formats using Chemcraft and subsequently processed through Notepad to organize the codes, which were later tabulated for analysis. The generated SMILES strings were then input into the respective platforms, and the resulting data were systematically analyzed to evaluate the pharmacological potential of the compounds.

Results and discussion

Characterization of the synthesized compounds

To arrive at the chemical structures shown in Figs. 1 (a-d), FT-IR, ¹H-NMR, ¹³C-NMR and High-Resolution Mass Spectrometry (HRMS) were employed in the elucidation of the synthesized compounds. In the FT-IR spectrum, the hydroxyl group absorption bands were visible between 3734 and 3564 cm^− 1, N – H of amide were observed at 3374–3229 cm^− 1. Notable absorption bands were displayed between 1868 and 1685 cm^− 1 which corresponds to two carbonyls of the amide group, the C = C of aromatic ring showed absorption between 1576 and 1521 cm^− 1. At 1370–1321 cm⁻¹indicated the presence of S = O of sulphonamide groups. In the ¹H-NMR, two significant proton peaks of N-H appeared at 12.86–9.68 ppm as singlets and doublets, aromatic protons exhibited as multiplets between 8.05 and 6.52 ppm. Four methine protons of the threonine dipeptide were observed as multiplets at 4.98–3.99 ppm due to the neighboring methyl group. Three methyl peaks of the para toluene sulphonamide and threonine groups showed signals at 3.43–1.16 ppm as singlets and multiplets respectively. In the ¹³C-NMR, the resonance signal of the two carbonyl carbons appeared between 171.87 and 168.26 ppm. Aromatic carbon peaks were displayed at 155.84–109.85 ppm. Aliphatic carbon signals appeared upfield at 68.08–13.14 ppm. The HRMS confirmed the molecular structure of the target molecules with a molecular ion peak (M + Na)⁺ between 550.0621 and 452.1834.

Geometry optimization

Sulfonamides constitute a critical class of compounds with wide-ranging biological and pharmaceutical applications. Their molecular geometry, particularly the bond lengths and bond angles around the sulfonyl group, plays a decisive role in dictating their electronic, steric, and intermolecular interactions. Understanding these parameters through computational or crystallographic studies provides insight into their reactivity, stability, and binding properties in biological systems. Figure 2 shows the structures, and the values presented in Table 1 show a comparison of bond lengths and bond angles for different studied compounds, CPD-1, CPD-2, CPD-3, and CPD-4, after geometry optimization. In this study, four sulfonamide compounds (CPD-1 to CPD-4) were analyzed in terms of bond lengths and bond angles, and the results were compared with reported values from crystallographic and computational studies in the recent literature. The data obtained for CPD-1 to CPD-4 reveal that the S = O bond lengths lie within the range of 1.43–1.44 Å (e.g., CPD-2 and CPD-3), while S–N bond lengths vary between 1.64 and 1.69 Å. The S–C bonds are typically observed at 1.75–1.79 Å. Bond angles around the sulfonyl group show some variation: O–S–O angles approach 120° in CPD-3, while N–S–C and related angles are more compressed, ranging from 103° to 110°. The C–N–C and C–C–N angles within the aromatic or heterocyclic systems remain larger (123°–129°), consistent with sp² hybridization. The observed bond lengths and angles broadly agree with those reported for sulfonamide derivatives in crystallographic and computational studies. Crystallographic investigations of benzene sulfonamides indicate S = O bond distances of ~ 1.428–1.441 Å and S–N bonds of ~ 1.618–1.640 Å^40,41. Similarly, computational studies on thiophene sulfonamide derivatives report S–N bond lengths of 1.67–1.68 Å and S = O distances of 1.45 Å, further supporting the experimental ranges observed in this study⁴². The S–C bond lengths in our analysis (~ 1.75–1.79 Å) are consistent with typical aromatic sulfonamide substituents, as reported by Stenfors et al. (2020b). The O–S–O bond angle is generally reported to be 118°–120° in sulfonamides⁴⁰, which closely matches the values obtained for CPD-3 (120.21°). However, deviations are observed in CPD-2, where the O–S–N angle narrows to 107.98°, suggesting steric or electronic perturbations. Similarly, the elongated S–N bond in CPD-1 (1.69 Å) exceeds typical crystallographic averages, which may reflect decreased double-bond character, steric hindrance, or hydrogen-bonding effects involving the sulfonamide nitrogen. The consistency between the present findings and published crystallographic data confirms the structural reliability of the sulfonamide moiety across different derivatives. Nevertheless, the observed deviations particularly the elongated S–N bonds and compressed N–S–C angles in CPD-1 and CPD-2 highlight the influence of local substituents and intramolecular constraints on geometry. Such perturbations have been noted in earlier studies, where bulky or electron-withdrawing substituents led to stretching of the S–N bond and distortion of bond angles around the sulfur atom^41,42. These structural modulations are relevant in drug design, as bond length alterations and angular deviations can influence binding affinity and molecular recognition in enzyme or receptor pockets. The structural characterization of sulfonamide derivatives CPD-1 to CPD-4 demonstrates that their bond lengths and bond angles fall largely within established ranges reported for sulfonamides, with minor deviations attributable to substituent effects. The S = O and S–C bond distances are in excellent agreement with literature values, while some elongation of the S–N bond and narrowing of N–S–C bond angles suggest steric or electronic influences unique to specific compounds.

Fig. 2.

Geometry optimized structures of Sulfonamides derivatives (CPD-1, CPD-2, CPD-3, and CPD-4).

Table 1.

Geometrical properties (bond length and angle) of the studied sulfonamide derivatives.

Compound	Bonding atoms	Bond angle atoms	Bond length (Å)	Bond-angle
CPD-1	C27-C26	C27-C26-N23	1.40	123.53
	N23-C21	N23-C21-O24	1.36	124.79
	N10-S7	N10-S7-O9	1.69	108.72
	Cl32-C30	Cl32-C30-C28	1.75	119.85
CPD-2	O8-S7	O8-S7-N10	1.43	107.98
	C21-N23	C21-N23-C26	1.37	129.87
	C28-C30	C28-C30-O32	1.39	124.60
	N10-S7	N10-S7-C5	1.64	103.97
CPD-3	O8-S7	O8-S7-O9	1.44	120.21
	N16-C15	N16-C15-O17	1.36	121.12
	C30-C26	C30-C26-N23	1.52	109.55
cpd-4	N23-C23	N28-C23-C24	1.32	123.64
	O20-C14	O20-C14-C13	1.20	121.85
	S8-C5	S8-C5-C4	1.79	119.97

Electronic properties

Frontier molecular orbital analysis

The frontier molecular orbital (FMO) theory provides valuable insights into the chemical reactivity, stability, and bioactivity of drug-like molecules by evaluating the energy levels of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), along with related descriptors such as ionization potential (I), electron affinity (A), chemical hardness (η), softness (S), chemical potential (µ), and electrophilicity index (ω). These descriptors reveal how easily a molecule can donate or accept electron density, its resistance to charge redistribution, and its potential to engage in charge-transfer interactions during binding to biomacromolecules. For bioactive sulfonamides, FMO descriptors are crucial for predicting binding to ulcer-related targets such as proton pumps or enzymes that regulate gastric acid secretion^43,44. The computed FMO parameters for the four substituted sulphonamides as seen in Tables 2, 3 show distinct electronic characteristics. CPD-2 exhibited the smallest HOMO–LUMO gap (3.27 eV), the lowest hardness, and the highest electrophilicity (ω ≈ 5.956), indicating that it is the softest and most electronically polarizable of the set. CPD-3 (ΔE = 4.667 eV) and CPD-1 (ΔE = 4.869 eV) showed intermediate reactivity, while CPD-4 had the largest gap (7.477 eV) and the lowest electrophilicity (ω ≈ 2.39), making it the hardest and most electronically stable compound. These numerical values imply that CPD-2 is most likely to engage in charge-transfer and polarization-driven interactions in a protein active site, whereas CPD-4 would depend primarily on steric and geometric complementarity for binding. These trends are supported by recent computational studies on sulfonamide derivatives. Abedin and Pal (2024) demonstrated that FMO-derived descriptors such as softness and electrophilicity correlate strongly with docking scores in sulfonamide Schiff bases, showing that softer molecules with high electrophilicity interact more effectively with protein residues⁴⁵. Similarly, Tamilselvi (2024) reported that sulfonyl compounds with lower HOMO–LUMO gaps and higher electrophilicity indices exhibited improved binding affinities through enhanced charge-transfer capability⁴⁶. In another study, Abdelgawad et al. (2022) found that sulfamethoxazole derivatives with intermediate reactivity descriptors displayed selective inhibition of Carbonic Anhydrase isoforms, suggesting that compounds with moderate ΔE and ω values may balance binding strength with biological selectivity⁴⁷. This observation aligns with the electronic profiles of CPD-1 and CPD-3, which combine moderate gaps with strong local donor/acceptor orbitals to support stable binding. Eze et al. (2022) further confirmed that sulfonamide frameworks with small HOMO–LUMO gaps tend to localize orbital density around oxygen and nitrogen atoms, favoring charge transfer and hydrogen bonding⁴⁸. This supports the case of CPD-2, which, based on its descriptors, is predicted to form strong charge-assisted hydrogen bonds with nucleophilic residues in ulcer-relevant proteins. On the other hand, Arshad et al. (2024) showed that newly synthesized sulfonamide derivatives with higher hardness values bound more selectively to DNA and enzymes through shape-driven interactions, a pattern consistent with CPD-4’s large HOMO–LUMO gap and localized strong hydrogen bonding observed in docking⁴⁹. Recent reviews further contextualize these findings. Pal (2023) emphasized that electrophilicity is a robust global descriptor for predicting binding affinity, though high ω values may also correlate with toxicity risks⁴³. Ramírez-Martínez (2023) argued that a balance between softness and stability is often ideal in drug candidates, since very reactive molecules may suffer from metabolic liabilities. Applied to the present study, CPD-2’s high electrophilicity suggests strong potential for bioactivity but also warrants ADMET evaluation, while CPD-1 and CPD-3 appear to offer the most balanced profiles for anti-ulcer drug design. CPD-4, although electronically inert, may still serve as a selective binder where geometric complementarity dominates. Taken together, the FMO results show that the electronic properties of substituted sulfonamides are consistent with their predicted in silico anti-ulcer activity. As presented in Fig. 3, during the process of charge transfer, electrons move from the Highest Occupied Molecular Orbitals (HOMO) to the Lowest Unoccupied Molecular Orbitals (LUMO) in excited state, causing reactivity. CPD-2’s softness and high electrophilicity predispose it to strong charge-transfer interactions, CPD-1 and CPD-3 strike a balance between adaptability and stability, and CPD-4 favors shape-specific binding. These observations echo recent DFT/drug design literature and suggest that CPD-1and CPD-3 represent the most promising scaffolds for further computational and experimental evaluation as anti-ulcer agents.

Table 2.

FMO analysis: HOMO energy, LUMO energy, energy gap (Eg), chemical softness (σ), chemical hardness (η), chemical potential (µ), electrophilicity index (ω) of the studied compounds.

System	HOMO (eV)	LUMO (eV)	Energy gap	Ionization Potential (IP)	Electron Affinity (EA)	Electronegativity (χ)	Hardness (Ƞ)	Softness (σ)	Electrophilicity (ω)
CPD-1	−6.340	−1.471	4.869	6.340	1.471	3.906	2.435	1.217	2.406
CPD-2	−6.048	−2.778	3.27	6.048	2.778	4.413	1.635	0.306	5.956
CPD-3	−6.619	−1.952	4.667	6.619	1.952	4.286	2.334	0.214	3.935
CPD-4	−7.963	−0.486	7.477	7.963	0.486	4.2245	3.7385	1.869	2.387

Table 3.

Second-order perturbation energies for the studied systems.

COMPOUND	Donor (i)	Acceptor (j)	E(2) kcal/mol	E(j)-E(i) a.u.	F(i, j) a.u.	Transition
CPD-1	LP1-C22	LP1-C26	146.59	0.01	0.038	LP→LP
	C29-H57	LP1-C22	73.14	0.39	0.195	σ → Lp
	C5-S7	S7-N10	58.41	0.88	0.209	σ → σ*
	C5-S7	S7-O8	50.88	1.00	0.211	σ → σ*
CPD-2	C15-O17	C15-O20	177.54	0.96	0.372	σ → σ*
	C19-C29	C19-H34	225.01	1.80	0.372	σ → σ*
CPD-3	C11-C15	C15-O20	261.34	0.91	0.442	σ → σ*
	C11-C15	C15-O17	269.32	1.28	0.542	σ → σ*
CPD-4	C13 - C14	C15 - O17	104.56	0.91	0.278	σ → σ*
	O22 - H56	C31 - H33	89.17	0.91	0.267	σ → σ*
	O30 - H32	C29 - O30	149.28	1.30	0.394	σ → σ*
	O30	C29 - O30	119.80	0.97	0.307	σ → σ*

Fig. 3.

Structure of the studied compounds showing the iso-surface of the HOMO and LUMO.

Natural bond orbital

The NBO second-order perturbation data show where intramolecular donor→acceptor stabilization is concentrated. CPD-3 displays very large E⁽²⁾ stabilization energies (≈ 261.34 and 269.32 kcal·mol⁻¹), CPD-2 also has substantial terms (≈ 177.54, 225.01 kcal·mol⁻¹), while CPD-1 shows strong lone-pair activity (≈ 146.6, 73.14, 58.41, 50.88 kcal·mol⁻¹) and CPD-4 presents moderate E(2) values (≈ 149.3, 119.8 kcal·mol⁻¹). These findings emphasize that CPD-2 and CPD-3 have highly conjugated, electronically adaptive structures, CPD-1 is rich in lone-pair donors/acceptors, and CPD-4 is relatively rigid (data drawn directly from your file). Recent NBO-based computational studies confirm that high stabilization energies at sulfonyl O and sulfonamide N atoms predict strong hydrogen-bonding capacity in docking. Abedin and Pal (2024) reported that sulfonamide Schiff bases with high donor–acceptor stabilization showed strong polar interactions in silico⁵⁰. Likewise, Tamilselvi (2024) highlighted that σ→σ* and LP→σ* delocalization channels enhance adaptability for hydrogen bonding in drug-target interactions⁵¹. These results suggest that CPD-2 and CPD-3 are most flexible in distributing electron density within a protein active site, CPD-1 offers strong lone-pair-mediated H-bonding potential, and CPD-4 may act more as a rigid shape-specific binder⁵².

Molecular electrostatic potential (MESP) analysis

The molecular electrostatic potential surface (MESP) shown in Fig. 4 provides valuable insight into how charges are distributed across a molecule, highlighting reactive regions that influence binding in drug design. Red regions represent areas of high negative potential, generally located around electronegative atoms such as oxygen and nitrogen and serve as favourable sites for electrophilic attack. Conversely, blue regions correspond to areas of positive potential, typically found near hydrogen atoms attached to heteroatoms like nitrogen or oxygen, marking potential sites for nucleophilic attack or hydrogen bond donation. Green zones indicate nearly neutral electron density, associated with weak van der Waals interactions, while yellow and orange regions display moderate negative potential, signifying intermediate reactivity. MESP mapping is particularly important in biological systems because it pinpoints the reactive hot spots that determine how a drug candidate interacts with proteins, enzymes, or nucleic acids. In a computational study of {(4-nitrophenyl) sulfonyl} tryptophan, Eze et al.. (2022) showed that oxygen atoms consistently carried significant negative potential while hydrogen atoms near heteroatoms exhibited positive regions, aligning with predicted biological binding sites⁴⁸. Similarly, Abdelgawad et al. (2022) investigated sulfamethoxazole derivatives and demonstrated that MESP surfaces revealed negative/positive potential patches corresponding to active binding sites on target enzymes, underscoring the predictive value of electrostatic potential in drug–target interactions⁴⁷. Extending this approach, Arshad et al. (2024) compared sulfonamide derivatives and found that variations in charge distribution correlated strongly with enzyme inhibition and DNA binding activity, confirming that compounds with well-defined negative and positive zones exhibit improved activity and selectivity⁴⁹. Other recent reports reinforce these findings. Shahid et al. (2023) studied thiazole-based sulfonamide hybrids and observed that negative regions around oxygen atoms and positive potentials near heteroatom-bonded hydrogens dictate binding modes by favoring complementary electrostatic contacts with amino acid residues⁵³. Likewise, Morales-Pumarino and Barquera-Lozada (2023) emphasized the ability of MESP to identify electrophilic and nucleophilic regions in bioactive molecules, with larger positive and negative potentials directly correlating with stronger interactive behavior of functional groups such as O, N, and S⁵⁴. Taken together, these studies confirm that MESP is a reliable computational tool for visualizing charge distribution and predicting bioactive behavior. For anti-ulcer drug design, sulfonamide derivatives such as CPD-1 benefit from possessing both electron-rich (red) and electron-deficient (blue) regions. This dual character enhances their ability to form complementary interactions, electrophilic, nucleophilic, and hydrogen bonding with protein residues in ulcer-related biological targets, ultimately improving activity and selectivity.

Fig. 4.

Molecular electrostatic potential (MESP) for the compounds.

Noncovalent interaction (NCI) analysis

The Non-Covalent Interaction (NCI) iso-surface of the studied compounds is shown in Fig. 5 and reveals the regions where weak intermolecular forces occur, which play a vital role in their biological activity. In the plot, blue regions represent strong attractive forces such as hydrogen bonding, green areas indicate weak Vander Waals interactions, while red areas correspond to steric repulsion. The blue zones observed around hydrogen and electronegative atoms suggest potential hydrogen-bond donor and acceptor sites, which are critical for binding to biological targets such as proteins or enzymes. The green dispersive interactions contribute to molecular stability and help the compound fit snugly into binding pockets, while the red repulsive zones may limit certain conformations, influencing selectivity. Khan et al. 2025 further confirmed that sulfonamide-linked heterocycles rely heavily on a combination of blue (hydrogen bonding) and green (van der Waals) interactions for their antiulcer binding modes, while red steric zones enhanced selectivity by preventing non-specific interactions⁵⁵. Such an arrangement of attractive and stabilizing forces enhances the compound’s ability to recognize and interact specifically with its target, a property essential for effective biological function^56,57.

Fig. 5.

3D Visualized NCI plots for studied compounds.

Localized orbital locator (LOL) and electron localization function (ELF) analysis

The Electron Localization Function (ELF) and Localized Orbital Locator (LOL) analyses were employed to investigate the electron density distribution, bonding characteristics, and intramolecular interactions within the synthesized compounds CPD-1 to CPD-4. ELF and LOL are reliable quantum chemical descriptors that provide complementary insights into electron localization, where values approaching unity indicate highly localized electrons associated with covalent bonding or lone pairs, while lower values correspond to delocalized or weakly interacting regions^58,59. The analyses were performed at selected bond critical points (BCPs) to elucidate the nature and strength of interactions present in each compound. For CPD-1, relatively low LOL and ELF values were observed for interactions such as O₃₀–O₂₀ (LOL = 0.123, ELF = 0.193) and C₂₉–H₆₁ (LOL = 0.264, ELF = 0.114), indicating weak noncovalent or through-space interactions with limited electron sharing. The H₅₀–H₄₃ interaction exhibited moderately higher localization (LOL = 0.437, ELF = 0.204), suggesting electron density concentration arising mainly from steric proximity rather than strong bonding. CPD-1 shows comparatively lower electron localization, reflecting a more flexible electronic environment. In CPD-2, the ELF and LOL values reveal enhanced electron localization associated with intramolecular hydrogen bonding. Interactions such as H₃₆–O₁₇ and O₁₇–H₃₄ show moderate ELF values (0.304 and 0.396, respectively), while the O₂₁–H₃₂ interaction displays a notably high ELF value of 0.706, indicating a strongly localized hydrogen bond. These results suggest the presence of a well-defined hydrogen-bonding network within CPD-2, which may contribute to its conformational stability and interaction potential. CPD-3 exhibits the highest degree of electron localization among the studied compounds. The H₃₉–C₂₂ interaction shows very high LOL (0.913) and ELF (0.991) values, characteristic of strong covalent bonding with highly localized electrons. Other interactions in Table 4, such as O₁₇–C₆ and H₄₃–H₃₁, display lower values, corresponding to weaker secondary interactions. The dominance of highly localized electron density in CPD-3 indicates a rigid molecular framework, which can influence its binding behavior in biological systems. Similarly, CPD-4 demonstrates pronounced electron localization, particularly in the C₂₇–C₂₈ bond, which exhibits high LOL (0.786) and ELF (0.931) values indicative of strong covalent character. The O₉–C₂₈ interaction shows intermediate localization, consistent with a polar covalent bond, while the O₉–H₆₁ interaction presents moderate ELF values (0.355), suggesting hydrogen bonding or polar interaction. This combination reflects a balance between molecular rigidity and the ability to engage in polar interactions. The two-dimensional ELF contour plots in Fig. 6 further corroborate these findings by illustrating regions of high electron localization around heteroatoms and covalent bond regions, as well as moderate localization in hydrogen-bonding zones, particularly in CPD-2 and CPD-4. In contrast, CPD-1 exhibits more diffuse ELF distributions, consistent with weaker and more delocalized interactions.

Table 4.

Molecular docking results: Depicting amino acid residues, binding affinity (kcal/mol), bond distance (Å), and number of hydrogen bonds within the protein-ligand interactions.

COMP.	Amino acid residue	Binding affinity (Kcal/mol)	Bond distance (Å)	Type of interaction	Number of H-bond
CPD-1 + 8AK1	A: ASN47:HD21	−7.2	2.09156	Conventional Hydrogen	5
	A: ASN47:HD22	−7.2	2.75114	Conventional Hydrogen
	:UNK0:H	−7.2	2.73807	Conventional Hydrogen
	:UNK0:H	−7.2	2.0868	Conventional Hydrogen
	:UNK0:H	−7.2	2.259	Conventional Hydrogen
CPD-2 + 8AK1	A: ASN47:HN	−6.3	2.19328	Conventional hydrogen	3
	A: ASN47:HD22	−6.3	2.81506	Conventional hydrogen
	:UNK0:H	−6.3	2.11628	Conventional hydrogen
CPD-3 + 8AK1	A: ASN47:HN	−6.3	2.36408	Conventional hydrogen	3
	:UNK0:H	−6.3	2.61865	Conventional hydrogen
	:UNK0:H	−6.3	1.95828	Conventional hydrogen
CPD-4 + 8AK1	:UNK0:O	−4.8	3.11754	Conventional hydrogen	2
	:UNK0:H	−4.8	1.86096	Conventional hydrogen
DB00338 + 8AK1	A: ASN47:HD21 - :UNK0:N	−5.8	1.94166	Conventional hydrogen	1

Fig. 6.

Pictorial representation of the 2D View interaction between the investigated ligand (CPD-1, CPD-2, CPD-3 and CPD-4) and the studied receptor (8AK1).

Table 4.

Bond critical points (BCPs) and corresponding LOL and ELF values for selected intramolecular interactions in CPD-1 to CPD-4.

Compound	BCPs	Interactions	LOL (a.u)	ELF (a.u)
CPD-1	68	O30-O20	0.123	0.193
	87	C29-H61	0.264	0.114
	126	H50-H43	0.437	0.204
CPD-2	103	H36-O17	0.151	0.304
	109	O21-H32	0.216	0.706
	120	O17-H34	0.169	0.396
CPD-3	60	H39-C22	0.913	0.991
	103	O17-C6	0.133	0.231
	111	H43-H31	0.162	0.358
CPD-4	127	O9-C28	0.588	0.384
	130	C27-C28	0.786	0.931
	134	O9-H61	0.567	0.355

Fig. 6.

Two-dimensional ELF contour plots illustrating electron localization and interaction regions in CPD-1, CPD-2, CPD-3, and CPD-4.

Molecular docking analysis

Rationale for selecting PDB ID: 8AK1

PDB ID 8AK1 was selected due to its high-resolution X-ray structure (1.84 Å) and excellent validation statistics, ensuring structural reliability for molecular docking studies. Although the structure contains two protein chains, only chain B (Cag19) was used for docking, as it represents the biologically relevant target protein^60,61. Chain A corresponds to a designed ankyrin repeat protein employed solely as a crystallization scaffold and was excluded to avoid non-physiological interactions and docking artefacts.

Molecular docking discussion

Molecular docking was carried out to evaluate the binding interactions of sulfonamide derivatives CPD-1, CPD-2, CPD-3, and CPD-4 with the 8AK1 protein in comparison to the standard drug DB00338. The docking scores revealed that some of the derivatives generally displayed stronger binding affinities than the reference compound. Among them, CPD-1 exhibited the most favorable binding energy (− 7.2 kcal/mol), followed by CPD-2 and CPD-3 with affinity (− 6.3 kcal/mol) respectively; CPD-4 displayed a very low affinity (− 4.8 kcal/mol). In contrast, DB00338 had a binding affinity of − 5.8 kcal/mol. These findings suggest that some of the designed sulfonamides may provide superior inhibitory activity compared to the standard drug, as docking energies below − 6.0 kcal/mol are often associated with promising lead compounds⁶². Analysis of hydrogen bond interactions provided deeper insights into the stabilization of these ligand–protein complexes. CPD-1 formed five conventional hydrogen bonds with key residues, including A: ASN47:HD21, A: ASN47:HD22, and: UNK0:H with bond lengths ranging from 2.09 to 2.97 Å. CPD-2 and CPD-3 established three hydrogen bonds involving A: ASN47:HN and: UNK0:H, respectively, suggesting extensive stabilization within the binding pocket, while CPD-4 established two hydrogen bonds all involving: UNK0:H. In contrast, DB00338 was anchored by only one hydrogen bond with A: ASN47:HD21 at a short bond length of 1.94 Å, indicating a localized but less stable interaction. Conventional hydrogen bonds are widely recognized as critical contributors to drug–target recognition and stability. Their directional nature provides specific anchoring of ligands to conserved polar residues, thereby enhancing both affinity and selectivity⁶³. The repeated involvement of residue ASN47 across several sulfonamide derivatives highlights this amino acid as a hydrogen bonding hotspot crucial for ligand stabilization within 8AK1. Moreover, multiple hydrogen bonds not only increase enthalpic stabilization but also improve desolvation by replacing structured water molecules in the active site, which further enhances binding affinity⁶⁴. Quantum mechanical analyses have also confirmed that such networks significantly influence drug potency by reinforcing complementarity at the molecular interface⁶⁵. Based on the docking data as recorded in Table 4, CPD-1 is predicted to be the most potent inhibitor of 8AK1, supported by both its superior binding energy and the formation of five stabilizing hydrogen bonds. CPD-2 and CPD-3 are also strong candidates given their hydrogen bonding network, despite slightly weaker binding energy than CPD-1. In comparison, DB00338 is likely to be less effective against 8AK1 due to its weak overall interaction profile. These findings align with recent docking-based studies demonstrating that compounds with more negative docking scores and multiple well-oriented hydrogen bonds tend to show enhanced inhibitory potential in vitro⁶⁶. The compound-protein interactions were shown in Figs. 6 (2D) and 7 (3D), respectively. Taken together, this study underscores the importance of conventional hydrogen bonding in mediating sulphonamide-8AK1 interactions and highlights CPD-1, CPD-2 and CPD-3 as promising candidates for further development.

Fig. 7.

Pictorial representation of the 3D view interaction between the investigated ligand (CPD-1, CPD-2, CPD-3 and CPD-4) and the studied receptor (8AK1).

ADMET studies

The ADMET properties of the four studied sulfonamide derivatives (CPD-1–CPD-4) were evaluated using in silico computational approaches. Parameters assessed included absorption (Caco-2 and MDCK permeability, P-glycoprotein inhibition), distribution (plasma protein binding [PPB], volume of distribution [Vd], blood-brain barrier penetration), metabolism (CYP450 isoform inhibition), excretion (clearance and half-life), physicochemical properties (molecular weight, topological polar surface area [TPSA], Lipinski’s rule compliance), and toxicity (hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, cytotoxicity). Predictions were generated through cheminformatics-based models employing quantitative structure–activity relationship (QSAR) algorithms and validated datasets, following approaches like those used in recent pharmacokinetic modeling and machine learning-based ADMET prediction studies^67,68. The ADMET results as shown in Table SI 1 of the supporting document revealed variable pharmacokinetic performance across CPD1–CPD4, with absorption posing the greatest challenge. All compounds demonstrated poor Caco-2 permeability (log Papp < − 5.6), suggesting limited intestinal absorption. This trend has also been reported in recent sulfonamide-based anti-ulcer candidates, where prodrug strategies were recommended to improve uptake⁶³. MDCK permeability values, however, were within acceptable ranges, indicating potential for transcellular transport, a finding consistent with recent machine learning predictions of epithelial permeability⁶⁹. Distribution analysis showed that CPD-1 and CPD-3 had excessively high PPB values (> 90%), which may restrict free drug availability. In contrast, CPD-2 and CPD-4 maintained moderate-to-low PPB (54.9% and 13.2%, respectively), closer to the optimal range (50–60%) identified in pharmacokinetic optimization of benzimidazole analogues. Volumes of distribution for all compounds remained favorable, aligning with reported values for clinically used proton pump inhibitors (PPIs)⁷⁰. Regarding metabolism, none of the compounds showed inhibition of major CYP isoforms (CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4), minimizing risks of drug–drug interactions. This is consistent with the desirable metabolic profile of recent flavonoid-based anti-ulcer leads, which were similarly non-inhibitory toward CYP450 enzymes. Excretion profiles highlighted clearance variability, with CPD-1 showing the lowest clearance (0.693 mL/min/kg), whereas CPD-3 showed the highest (4.133 mL/min/kg). Moderate clearance, as seen in CPD-2 (2.24 mL/min/kg), is desirable to ensure sufficient systemic exposure, in line with pharmacokinetic modeling of ilaprazole and other PPIs⁷¹. The half-lives of CPD1–3 was extremely short (< 1 s), raising concerns about therapeutic efficacy. In contrast, CPD4 showed an extended half-life (2.34 s), though still significantly shorter than the clinically relevant half-lives (> 1 h) reported for stabilized PPI analogues⁷². Such discrepancies suggest the need for structural modification or formulation strategies (e.g., controlled release) to extend exposure, as emphasized in recent systematic reviews of potassium-competitive acid blockers (P-CABs)⁷³. Toxicity profiling predicted all compounds to be inactive for hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity, supporting their potential safety for long-term ulcer therapy. Comparable in silico safety predictions have been reported for both phytochemical-based anti-ulcer agents⁷⁴ and synthetic sulfonamide conjugates⁷⁵, reinforcing the viability of this structural class for gastroprotective drug design. Taken together, CPD-2 emerges as the most promising candidate, balancing moderate PPB, acceptable clearance, Lipinski compliance, and predicted safety. CPD-1 and CPD-3 are penalized by excessive protein binding and extremely short half-lives, while CPD-4 demonstrates low PPB but suffers from a Lipinski violation that may limit oral bioavailability. Comparative evaluation with recent anti-ulcer drug development efforts underscores that optimization of absorption and half-life remains the primary challenge for advancing sulfonamide derivatives as anti-ulcer therapeutics.

Conclusion

This study employed a multi-level computational strategy to investigate four novel sulfonamide derivatives (CPD-1 to CPD-4) as potential anti-ulcer agents targeting H. pylori. Geometry optimizations with B3LYP, ωB97XD, and M06-2X functionals confirmed structural stability across all derivatives. Frontier molecular orbital (FMO) analysis revealed relatively small HOMO–LUMO energy gaps, with CPD-1 showing the lowest gap (3.71 eV), indicating its higher reactivity and potential biological activity. Natural bond orbital (NBO) calculations demonstrated strong intramolecular charge transfer and stabilization effects. Molecular electrostatic potential (MESP) mapping highlighted distinct electrophilic and nucleophilic regions, guiding possible interaction sites with the target protein, while non-covalent interaction (NCI) plots confirmed stabilization through hydrogen bonding and van der Waals interactions. Molecular docking against H. pylori CagI protein (PDB ID: 8AK1) showed that CPD-1 achieved the strongest binding affinity (− 7.2 kcal/mol), forming five key hydrogen bonds with active-site residues, surpassing the reference drug DB00338. CPD-2 and CPD-3 also exhibited strong affinities (− 6.3 kcal/mol, respectively), demonstrating competitive binding potential. ADMET predictions indicated that CPD-2 possessed favorable pharmacokinetic and safety profile, with good oral bioavailability, moderate plasma protein binding, acceptable clearance, and compliance with Lipinski’s rule of five. Based on these computational findings, these findings identify CPD-1 as the most potent inhibitor candidate due to its superior binding and electronic properties, while CPD-2 emerges as the most druggable scaffold owing to its balanced pharmacokinetic characteristics. CPD-3 also presents a promising balance of binding affinity and drug-likeness.

Although the present study provides valuable insights into the electronic properties and in silico interaction profiles of the synthesized p-toluene sulphonamide derivatives, it is important to acknowledge the inherent limitations of computational predictions. Molecular docking and DFT-based analyses offer a static and theoretical evaluation of ligand–protein interactions and do not fully account for the dynamic nature of biological systems, solvent effects, or complex cellular environments. Consequently, the predicted anti-ulcer activity should be regarded as preliminary. Experimental validation through biochemical assays, cellular studies, and in vivo anti-ulcer models will be essential to confirm the pharmacological relevance of the proposed compounds. Future work will therefore focus on conducting appropriate experimental evaluations to validate the computational findings and to further explore the therapeutic potential of these derivatives.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Joy, Nkechi Orji, Fredrick C. Asogwa and Izuchukwu, David Ugwu: Conceptualization, Design, Methodology, Validation, Resources, Results analysis and editing, Joel I. Aondoungwa, Chinelo Ogechi Ekoh, Favour C. Maduka and Chioma G. Kalu: Result analysis and manuscript writing. Chris Uchechukwu Okoro, and Destiny E. Charlie: Methodology and Experimental Design. Rafat Ali: Resources.

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.